Planta Med 2011; 77 - WSIV4
DOI: 10.1055/s-0031-1282114

Fishing and Knockout (FAK) Strategy for Quality Control of Traditional Chinese Medicines (TCMs)

P Li 1, L Qi 1, G Xin 1
  • 1State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 210009, China

Herbs and preparations have been the basis of traditional Chinese medicines (TCMs) for thousands of years, and continue to be considered valuable composition in health care system. One of the biggest challenges of herbal research is the complexity of its chemical constituents and their analysis. Thus, a wide range of analytical methods need to be used to fully characterize these 'magical components' and used for quality control (QC) purpose. The effects of TCMs are, of course, induced by their chemical constituents. For effective quality control and efficacy evaluation, we should know: What exist in TCMs? Which are biologically active? And how the components induce integrative functions? During the last decade, our research team used multidisciplinary theory and technology to develop novel methods for multi-component and multi-target evaluation of TCMs. First, a diagnostic ion filtering strategy was proposed for rapid screening and identification of non-target compounds in complex TCM samples by UPLC-Q-TOF/MS. Next, a novel chemical markers' fishing and knockout (FAK) strategy was proposed for screening bioactive compounds and studying component-component interactions. A two-dimensional turbulent flow chromatography on-line coupled with LC-MS strategy was introduced to screen out the bioactive compounds from TCMs binding target proteins. Then, to prove whether the obtained/assumed constituents are active compounds for the extract, a chemical markers' knockout strategy was recommended to prepare an extract in which the assumed compound(s) is removed, called a ''knockout'' extract. After bioactivity comparison of samples with various components-knockouts or target compounds collections, the fingerprinting-efficacy relationship of TCMs, component-component interactions could be predicted by statistic analysis. With collaborative expertise in chemistry, chemometrics, biology, and bioinformatics, we believe that there are good prospects for obtaining new insights into drug discovery and clinical utility by the continued study of TCMs.

Acknowledgements: This work was supported in part by the National Key Technologies R&D Program of China (No. 2008BAI51B01), Program for Changjiang Scholars and Innovative Research Teams in Universities (No. IRT0868).